Summary

This project provides AI agent tutorials for backend developers, specifically targeting those with Golang experience but no prior LLM background. It aims to demystify AI agent principles by implementing core mechanisms like Agent Loops, Tool Calling, Memory, and RAG from scratch in Go, focusing on practical engineering and design trade-offs rather than complex mathematical theory. The benefit is a hands-on understanding of how to build and integrate AI agents, serving as a strong foundation for prototyping and further development.

How It Works

The project takes a "from scratch" engineering approach, using Go 1.24+ to implement key AI agent concepts. It breaks down LLM interactions, starting with raw HTTP and SDK usage, then progressing to Function Calling for tool integration, and establishing an Agent Loop (Reasoning + Action). Advanced topics include context engineering for managing limited LLM windows, memory systems for long-term state, and Agentic RAG for autonomous information retrieval. The core advantage lies in its backend-centric perspective, explaining design choices and potential pitfalls encountered in real-world systems through runnable code examples.

Quick Start & Requirements

• Primary install/run command: go run <chapter_path>/main ... (e.g., go run ./ch01/main --stream -q "...")
• Prerequisites: Go 1.24 or higher, an LLM provider API Key (e.g., OpenAI, DeepSeek, GLM).
• Setup: Requires local Go installation and configuration of an .env file with the API key.
• Links: Project Address: https://github.com/baby-llm/baby-agent

Highlighted Details

• Comprehensive, step-by-step learning path covering LLM basics, Tool Calling, Agent Loops, Context Engineering, Memory, and Agentic RAG.
• Implementation of advanced AI concepts like ReAct, Agentic RAG with vector stores (pgvector), and a Skills System.
• System design elements include the MCP protocol for tool ecosystems and Guardrails for security, including Docker sandboxing.
• Focus on practical engineering aspects such as TUI visualization (Bubble Tea), LLM evaluation, and observability (Trace/Metrics/Log).

Maintenance & Community

Several advanced chapters (Web service, Server state, Evaluation, Production readiness) are currently in progress (marked with 🚧) and are continuously being updated. The project welcomes community contributions via Pull Requests.

Licensing & Compatibility

The project is licensed under the Apache License 2.0. This license is permissive and generally compatible with commercial use and linking in closed-source projects.

Limitations & Caveats

This repository is explicitly a teaching project, not an out-of-the-box production framework, and is not recommended for direct production deployment. It serves as a reference for principles and prototype validation, requiring significant additional engineering for real-world applications. Several key chapters are still under active development.